Abstract

An approach to the identification of minimum cost
solutions to regional particulate air quality problems is
described that is potentially more accurate and less resource-intensive than prior methods based on atmospheric
diffusion models. A receptor-oriented modeling procedure
is used to determine the source contributions to ambient
air quality at each monitoring site in an air basin. The air
quality model is matched to a linear programming algorithm
and is used to compute the least costly combination
of emission controls needed to meet air quality objectives.
The method is illustrated by application to the problem
of particulate air quality control using historically available data for the Los Angeles basin.